Marine Target Detection Using Dual-polarimetric SAR Imagery

In this study,we provide a summary of research advances in the field of maritime target detection using DP(dualpolarimetric)SAR(Synthetic Aperture Radar)imagery,accomplished during the European and China collaboration in the framework of the Dragon-4 program ID 32235.The main innovative contribution is twofold:(1)We addressed ship detection proposing an improved GP-PNF(Geometrical Perturbation-Polarimetric Notch Filter),termed as IGP-PNF,that is characterized by a new feature vector that includes three new scattering features;(2)We addressed oil platform detection by contrasting singlepolarization SAR methods with polarimetric ones in order to quantify the extra-benefit carried on polarimetric information.The proposed theoretical framework is tested against actual multi-polarization SAR data.In particular,ship detection methods are verified against a Sentinel-1 SAR scene where a large number of ships is present;while,oil platform detection is discussed using Terra SAR-X SAR data.Experimental analysis shows that:(1)The IGP-PNF method performs best in terms of clutter-to-target ratio;(2)Coherent polarimetric information significantly outperforms single-polarization SAR measurements in highlighting targets in challenging cases.

Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images

As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,for most common low-resolution radar plane position indicator(PPI)images,it is difficult to achieve good performance.In this paper,taking navigation radar PPI images as an example,a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background(e.g.,sea clutter)and target characteristics.The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network(CNN).First,to improve the accuracy of detecting marine targets and reduce the false alarm rate,Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects:new backbone network,anchor size,dense target detection,data sample balance,and scale normalization.Then,JRC(Japan Radio Co.,Ltd.)navigation radar was used to collect echo data under different conditions to build a marine target dataset.Finally,comparisons with the classic Faster R-CNN method and the constant false alarm rate(CFAR)algorithm proved that the proposed method is more accurate and robust,has stronger generalization ability,and can be applied to the detection of marine targets for navigation radar.Its performance was tested with datasets from different observation conditions(sea states,radar parameters,and different targets).

Improved TQWT for marine moving target detection

Under the conditions of strong sea clutter and complex moving targets,it is extremely difficult to detect moving targets in the maritime surface.This paper proposes a new algorithm named improved tunable Q-factor wavelet transform(TQWT)for moving target detection.Firstly,this paper establishes a moving target model and sparsely compensates the Doppler migration of the moving target in the fractional Fourier transform(FRFT)domain.Then,TQWT is adopted to decompose the signal based on the discrimination between the sea clutter and the target’s oscillation characteristics,using the basis pursuit denoising(BPDN)algorithm to get the wavelet coefficients.Furthermore,an energy selection method based on the optimal distribution of sub-bands energy is proposed to sparse the coefficients and reconstruct the target.Finally,experiments on the Council for Scientific and Industrial Research(CSIR)dataset indicate the performance of the proposed method and provide the basis for subsequent target detection.